



'''simple docstring'''






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowerCAmelCase:   Any					=		imread(r'digital_image_processing/image_data/lena_small.jpg')
lowerCAmelCase:   List[Any]					=		cvtColor(img, COLOR_BGR2GRAY)
def  lowerCamelCase__			(		):
  a		:   Union[str, Any]	  =	cn.convert_to_negative(_A							)
  # assert negative_img array for at least one True
  assert negative_img.any()
def  lowerCamelCase__			(		):
  with Image.open('digital_image_processing/image_data/lena_small.jpg'							) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(_A					,     110							)							).startswith(
        '<PIL.Image.Image image mode=RGB size=100x100 at'							)
def  lowerCamelCase__			(		):
  a		:   int	  =	canny.gen_gaussian_kernel(9					,     sigma=1.4							)
  # Assert ambiguous array
  assert resp.all()
def  lowerCamelCase__			(		):
  a		:   List[Any]	  =	imread('digital_image_processing/image_data/lena_small.jpg'					,     0							)
  # assert ambiguous array for all == True
  assert canny_img.all()
  a		:   int	  =	canny.canny(_A							)
  # assert canny array for at least one True
  assert canny_array.any()
def  lowerCamelCase__			(		):
  assert gg.gaussian_filter(_A					,     5					,     sigma=0.9							).all()
def  lowerCamelCase__			(		):
  # laplace diagonals
  a		:   int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]							)
  a		:   Tuple	  =	conv.img_convolve(_A					,     _A							).astype(_A							)
  assert res.any()
def  lowerCamelCase__			(		):
  assert med.median_filter(_A					,     3							).any()
def  lowerCamelCase__			(		):
  a  ,      a		:   List[str]	  =	sob.sobel_filter(_A							)
  assert grad.any() and theta.any()
def  lowerCamelCase__			(		):
  a		:   Any	  =	sp.make_sepia(_A					,     20							)
  assert sepia.all()
def  lowerCamelCase__			(		_A = "digital_image_processing/image_data/lena_small.jpg"							):
  a		:   Dict	  =	bs.Burkes(imread(_A					,     1							)					,     120							)
  burkes.process()
  assert burkes.output_img.any()
def  lowerCamelCase__			(		_A = "digital_image_processing/image_data/lena_small.jpg"					,     ):
  a		:   List[Any]	  =	rs.NearestNeighbour(imread(_A					,     1							)					,     400					,     200							)
  nn.process()
  assert nn.output.any()

def  lowerCamelCase__			(		):
  a		:   Any	  =	'digital_image_processing/image_data/lena.jpg'

  # Reading the image and converting it to grayscale.
  a		:   str	  =	imread(_A					,     0							)

  # Test for get_neighbors_pixel function() return not None
  a		:   Optional[int]	  =	0
  a		:   str	  =	0
  a		:   Optional[Any]	  =	image[x_coordinate][y_coordinate]

  a		:   Optional[Any]	  =	lbp.get_neighbors_pixel(
      _A					,     _A					,     _A					,     _A							)

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  a		:   Any	  =	np.zeros((image.shape[0], image.shape[1])							)

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0					,     image.shape[0]							):
    for j in range(0					,     image.shape[1]							):
      a		:   Dict	  =	lbp.local_binary_value(_A					,     _A					,     _A							)

  assert lbp_image.any()




'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    lowercase__					=       (
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    lowercase__					=       """document_qa"""
    lowercase__					=       AutoProcessor
    lowercase__					=       VisionEncoderDecoderModel

    lowercase__					=       ["""image""", """text"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    str		,     *__snake_case					:    Any		,     **__snake_case					:    List[Any]      ):
      if not is_vision_available():
        raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'      )

      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    "Image"		,     __snake_case					:    str      ):
      a		:   Union[str, Any]	  =	'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
      a		:   Any	  =	task_prompt.replace('{user_input}'		,     __snake_case      )
      a		:   Dict	  =	self.pre_processor.tokenizer(
          __snake_case		,     add_special_tokens=__snake_case		,     return_tensors='pt'      ).input_ids
      a		:   Tuple	  =	self.pre_processor(__snake_case		,     return_tensors='pt'      ).pixel_values

      return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







    def      lowercase_			( self					:    Any		,     __snake_case					:    int      ):
      return self.model.generate(
          inputs['pixel_values'].to(self.device      )		,     decoder_input_ids=inputs['decoder_input_ids'].to(self.device      )		,     max_length=self.model.decoder.config.max_position_embeddings		,     early_stopping=__snake_case		,     pad_token_id=self.pre_processor.tokenizer.pad_token_id		,     eos_token_id=self.pre_processor.tokenizer.eos_token_id		,     use_cache=__snake_case		,     num_beams=1		,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,     return_dict_in_generate=__snake_case		,     ).sequences







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Tuple      ):
      a		:   str	  =	self.pre_processor.batch_decode(__snake_case      )[0]
      a		:   str	  =	sequence.replace(self.pre_processor.tokenizer.eos_token		,     ''      )
      a		:   Union[str, Any]	  =	sequence.replace(self.pre_processor.tokenizer.pad_token		,     ''      )
      a		:   List[str]	  =	re.sub(r'<.*?>'		,     ''		,     __snake_case		,     count=1      ).strip()  # remove first task start token
      a		:   Dict	  =	self.pre_processor.tokenajson(__snake_case      )

      return sequence["answer"]